Stop guessing. Stop throwing content at the wall to see what sticks. And stop thinking that buying an expensive SEO tool subscription is a strategy in itself.

The biggest mistake I see agencies make isn’t a lack of data; it’s data paralysis. They have millions of rows of keywords, but no narrative. Or, they chase viral topics that have zero search volume.

To build a content engine that actually drives revenue, you need a unified workflow. You need to bridge the gap between what people are talking about now, what has long-term value, and how your site is actually performing.

I call this the Trinity of Traffic. It relies on three specific tools working in concert: Google Trends (Discovery), Semrush (Validation), and Google Search Console (Optimization).

Here is how to build the ultimate SEO workflow.

Phase 1: The Trend Spotter (Google Trends)

The Goal: Catch the wave before it breaks.

Most SEOs start with keyword research tools. The problem? Keyword tools rely on historical data. By the time a keyword shows a massive search volume in a database, the competition is likely already fierce.

Google Trends is your radar for the “now.” It allows you to identify breakout topics and seasonal shifts before your competitors do.

1. Identifying “Breakout” Topics

You aren’t looking for consistent volume here; you are looking for velocity.

• Go to Google Trends.
• Enter a broad seed keyword related to your niche (e.g., “AI Tools”).
• Filter by “Past 90 days” (not 12 months—you want recent spikes).
• Look at the “Related Queries” box. Switch the filter from “Top” to “Rising”.

Any query marked “Breakout” has seen search volume grow by over 5000%. These are your golden tickets. They represent a user need that is currently underserved by existing content.

2. The Comparative Analysis Trick

Never commit to a topic without checking the nomenclature. The way you describe a product might not be how the market describes it.

Actionable Tip: Use the “Compare” function.

If you are writing about remote work software, compare “Remote Work Tools” vs. “Work From Home Tools.”

• Blue Line: Remote Work Tools
• Red Line: Work From Home Tools

If the Red Line is consistently higher, that is your primary keyword. If the Blue Line is spiking upward while Red is flat, the market vernacular is shifting. Follow the spike.

Phase 2: The Validator (Semrush)

The Goal: Verify the data and size up the enemy.

You have a hunch from Google Trends. Now you need cold, hard metrics. This is where Semrush comes in. You need to know if that “breakout” topic is a flash in the pan or a viable traffic source with transactional intent.

1. Validating the Trend

Take the winning term from Phase 1 and plug it into the Semrush Keyword Magic Tool.

• Check Search Volume: Is there enough consistent traffic to justify the resource cost of writing a guide?
• Check Keyword Difficulty (KD%): If the KD is 85%+, do you have the Domain Authority to compete? If not, look for long-tail variations.

2. The “Sem rush” Gap Analysis

Whether you spell it Semrush or sem rush, the tool’s power lies in its ability to tell you exactly what you are missing.

Don’t just look at the keyword; look at who is ranking for it.

• Go to Organic Research.
• Enter the URL of the top ranking competitor for your target trend.
• Filter by “Pages”.

Look at their traffic distribution. If they have a page on this topic driving 10k visits a month, investigate their backlink profile for that specific URL. How many links do you need to win? Semrush gives you the “number to beat.”

Pro Tip: Look for the “Intent” column in Semrush. Even if a trend is hot, if the intent is purely “Informational” and you need “Commercial” leads, you may want to pivot the angle of your article to include a product comparison.

Phase 3: The Optimizer (Google Search Console)

The Goal: Polish the diamond and plug the leaks.

Once your content is live, Google Search Console (GSC) becomes your source of truth. Unlike third-party tools which estimate traffic, GSC tells you exactly what is happening.

1. Hunting for “Low-Hanging Fruit”

This is the fastest way to increase traffic without writing new content. You are looking for pages where Google is showing your content (Impressions), but users aren’t clicking (Low CTR).

• Go to Performance > Search Results.
• Filter for the last 3 months.
• Sort by Impressions (High to Low).
• Look for queries with high impressions but a CTR below 1.5% and an Average Position between 8 and 20.

The Fix:

These pages are on Page 2 or the bottom of Page 1. Google likes the content enough to rank it, but the snippet isn’t compelling, or the content lacks depth.

1. Rewrite the Title Tag and Meta Description to match the query intent.
2. Add a new H2 section to the article specifically targeting that query.

2. Fixing the Indexing Gaps

Use Search Console to monitor technical health. A page cannot rank if it isn’t indexed.

• Check the Pages > Not Indexed report.
• Look specifically for “Crawled – currently not indexed.”

This usually means Google saw the page but decided it wasn’t high-quality enough to include in the index. This is a massive red flag for content quality. Revisit these pages immediately—add original data, better images, or more word count.

At a Glance: The Tool Comparison

Here is how each tool functions within the Trinity workflow.

Frequently Asked Questions

Is Google Search Console free?

Yes. Google Search Console is 100% free and is arguably the most essential tool in any SEO stack. You verify ownership of your domain (usually via DNS record or HTML file), and Google provides you with the data.

Can I use Semrush instead of Google Trends?

Not exactly. While Semrush has a “Trending” filter, its core strength is historical data (averaged over months). Google Trends is real-time. If a news story breaks this morning, it will be on Trends immediately. It might take weeks to reflect accurately in Semrush. You need Trends for speed, and Semrush for depth.

How do I link Semrush to GSC?

Integrating them is a game-changer. It allows you to see all your organic data in one dashboard.

1. Log in to your Semrush project.
2. Go to the SEO Dashboard or Organic Traffic Insights.
3. Click “Connect Google Account.”
4. Select the Google email associated with your GSC property.
5. Allow access.Now, Semrush can analyze your “not provided” keywords by cross-referencing GSC data!

Your Next Step

Open Google Trends right now. Type in the core topic of your business. Change the time range to “Past 30 Days” and look at the Related Queries (Rising).

Find one breakout term.

Once you have it, take it to Semrush. If the volume is there, you have your next blog post topic. Executing this workflow once a week will do more for your traffic than checking your analytics daily ever will.

NVDA earnings beat expectations, fueling AI momentum, but Nvidia stock price shows investor caution.

Nvidia’s latest earnings report has once again underscored its central role in the global AI revolution. The chipmaker, whose GPUs power everything from generative AI models to advanced data centers, posted blockbuster results that exceeded Wall Street expectations. Yet, despite the strong NVDA earnings, the Nvidia stock price slipped, reflecting investor caution amid sky-high valuations and intense competition. According to Yahoo Finance, the company’s results remain one of the most closely watched indicators of AI’s commercial trajectory.

Key Earnings Highlights

For the fourth quarter of fiscal 2025, Nvidia reported record revenue of $39.3 billion, up 78% year-over-year. Data center sales, driven by surging demand for AI infrastructure, accounted for $35.6 billion, a 93% increase from the prior yearNVIDIA Newsroom. Earnings per share came in at $0.89, up 82% year-over-year.

On a full-year basis, Nvidia delivered $130.5 billion in revenue, more than doubling its performance from fiscal 2024. This growth cements Nvidia’s dominance in the AI hardware market, where its GPUs remain the backbone of large language models, autonomous systems, and enterprise AI adoption.

Expert and Market Reactions

Analysts on Yahoo Finance’s Market Catalysts noted that while Nvidia consistently beats estimates, its stock often reacts negatively due to lofty expectations. Antoine Chkaiban of New Street Research emphasized that five of the past eight earnings beats were followed by declines in Nvidia stock, as investors reassess valuations.

Investor sentiment remains mixed. On one hand, Nvidia’s results confirm its unrivaled position in AI infrastructure. On the other, concerns about sustainability, competition from rivals like AMD, and potential regulatory scrutiny weigh on market psychology.

NVDA Stock Price Analysis

Following the earnings release, NVDA stock price fell nearly 3%, closing at $181.08, down from a previous close of $186.60. Despite the dip, Nvidia shares remain up almost 28% over the past yearBenzinga, reflecting long-term confidence in its AI-driven growth story.

The volatility highlights a recurring theme: Nvidia’s earnings power is undeniable, but investor sentiment is sensitive to valuation risks. With a trailing P/E ratio above 50, the stock is priced for perfection, leaving little margin for error.

Forward-Looking AI Implications

Nvidia’s earnings reaffirm that AI is not just a technological trend but a revenue engine reshaping the semiconductor industry. The company’s GPUs are embedded in every layer of AI innovation—from cloud hyperscalers to startups building generative AI applications.

Looking ahead, analysts expect Nvidia’s revenue to continue climbing, with consensus estimates projecting EPS growth of more than 40% next year. However, the company must navigate challenges including supply chain constraints, intensifying competition, and geopolitical risks tied to chip exports.

Outlook

Nvidia’s latest earnings report demonstrates the company’s unmatched leverage in the AI economy. While NVDA earnings continue to impress, the Nvidia stock price reflects investor caution amid high expectations. For long-term shareholders, the trajectory remains promising: Nvidia is positioned as the indispensable supplier of AI infrastructure, a role that will likely define both its market value and the broader tech landscape.

In the months ahead, Nvidia’s ability to balance innovation with investor confidence will determine whether its stock can sustain momentum. As AI adoption accelerates globally, Nvidia’s role as the sector’s bellwether remains unchallenged.

The trillion-dollar LLM race is over. The true disruption will be Agentic AI—autonomous, goal-driven systems—a trend set to dominate TechCrunch Disrupt 2025.

When OpenAI’s massive multimodal models were released in the early 2020s, the entire tech world reset. It felt like a gold rush, where the only currency that mattered was GPU access, trillions of tokens, and a parameter count with enough zeroes to humble a Fortune 500 CFO. For years, the narrative has been monolithic: bigger models, better results. The global market for Large Language Models (LLMs) and LLM-powered tools is projected to be worth billions, with worldwide spending on generative AI technologies forecast to hit $644 billion in 2025 alone.

This single-minded pursuit has created a natural monopoly of scale, dominated by the five leading vendors who collectively capture over 88% of the global market revenue. But I’m here to tell you, as an investor on the ground floor of the next wave, that the era of the monolithic LLM is over. It has peaked. The next great platform shift is already here, and it will be confirmed, amplified, and debated on the hallowed stage of TechCrunch Disrupt 2025.

The future of intelligence is not about the model’s size; it’s about its autonomy. The next billion-dollar companies won’t be those building the biggest brains, but those engineering the most competent AI Agents.

🛑 The Unspoken Truth of the Current LLM Market

The current obsession with ever-larger LLMs—models with hundreds of billions or even trillions of parameters—has led to an industrial-scale, yet fragile, ecosystem. While adoption is surging, with 67% of organisations worldwide reportedly using LLMs in some capacity in 2025, the limitations are becoming a structural constraint on true enterprise transformation.

We are seeing a paradox of power: models are capable of generating fluent prose, perfect code snippets, and dazzling synthetic media, yet they fail at the most basic tenets of real-world problem-solving. This is the difference between a hyper-literate savant and a true executive.

Here is the diagnosis, informed by the latest ai news and deep-drives:

• The Cost Cliff is Untenable: Training a state-of-the-art frontier model still requires a multi-billion-dollar fixed investment. For smaller firms, the barrier is staggering; approximately 37% of SMEs are reportedly unable to afford full-scale LLM deployment. Furthermore, the operational (inference) costs, while dramatically lower than before, remain a significant drag on gross margins for any scaled application.
• The Reliability Crisis: A significant portion of users, specifically 35% of LLM users in one survey, identify “reliability and inaccurate output” as their primary concerns. This is the well-known “hallucination problem.” When an LLM optimizes for the most probable next word, it does not optimise for the most successful outcome. This fundamentally limits its utility in high-stakes fields like finance, healthcare, and engineering.
• The Prompt Ceiling: LLMs are intrinsically reactive. They are stunningly sophisticated calculators that require a human to input a clear, perfect equation to get a useful answer. They cannot set their own goals, adapt to failure, or execute a multi-step project without continuous, micro-managed human prompting. This dependence on the prompt limits their scalability in true automation.

We have reached the point of diminishing returns. The incremental performance gain of going from 1.5 trillion parameters to 2.5 trillion parameters is not worth the 27% increase in data center emissions and the billions in training costs. The game is shifting.

🔮 The TechCrunch Disrupt 2025 Crystal Ball: The Agentic Pivot

My definitive prediction for TechCrunch Disrupt 2025 is this: The main stage will not be dominated by the unveiling of a new, larger foundation model. It will be dominated by startups focused entirely on Agentic AI.

What is Agentic AI?

Agentic AI systems don’t just generate text; they act. They are LLMs augmented with a planning module, an execution engine (tool use), persistent memory, and a self-correction loop. They optimise for a long-term goal, not just the next token. They are not merely sophisticated chatbots; they are autonomous problem-solvers. This is the difference between a highly-trained analyst who writes a report and a CEO who executes a multi-quarter strategy.

Here are three fictional, yet highly plausible, startup concepts poised to launch this narrative at TechCrunch Disrupt’s Startup Battlefield:

1. Stratagem

• The Pitch: “We are the first fully autonomous, goal-seeking sales development agent (SDA) for B2B SaaS.”
• The Agentic Hook: Stratagem doesn’t just write cold emails. A human simply inputs the goal: “Close five $50k+ contracts in the FinTech vertical this quarter.” The Agentic AI then autonomously:
  • Reasons: Breaks the goal into steps (Targeting $\rightarrow$ Outreach $\rightarrow$ Qualification $\rightarrow$ Hand-off).
  • Acts: Scrapes real-time financial data to identify companies with specific growth signals (a tool-use capability).
  • Self-Corrects: Sends initial emails, tracks engagement, automatically revises its messaging vector (tone, length, value prop) for non-responders, and books a qualified meeting directly into the human sales rep’s calendar.
  • The LLM is now a component, not the core product.

2. Phage Labs

• The Pitch: “We have decoupled molecular synthesis from human-led R&D, leveraging multi-agent systems to discover novel materials.”
• The Agentic Hook: This startup brings the “Agent Swarm” model to material science. A scientist inputs the desired material properties (e.g., “A polymer with a tensile strength 15% higher than Kevlar and 50% lighter”). A swarm of specialised AI Agents then coordinates:
  • The Generator Agent proposes millions of novel molecular structures.
  • The Simulator Agent runs millions of physics-based tests concurrently in a cloud environment.
  • The Refiner Agent identifies the 100 most promising candidates, and most crucially, writes the robotics instructions to synthesise and test the top five in a wet lab.
  • The system operates 24/7, with zero human intervention until a successful material is confirmed.

3. The Data-Moat Architectures (DMA)

• The Pitch: “We eliminate the infrastructure cost of LLMs by orchestrating open-source models with proprietary data moats.”
• The Agentic Hook: This addresses the cost problem head-on. The core technology is an intelligent Orchestrator Agent. Instead of relying on a single, expensive, trillion-parameter model, the Orchestrator intelligently routes complex queries to a highly efficient network of smaller, specialized, open-source models (e.g., one for code, one for summarization, one for RAG queries). This dramatically reduces latency and inference costs while achieving a higher reliability score than any single black-box LLM. By routing a question to the most appropriate, fine-tuned, and low-cost model, they are fundamentally destroying the Big Tech LLM moat.

🏆 Why TechCrunch is the Bellwether

The shift from the LLM race to Agentic AI is a classic platform disruption—and a debut at Tech Crunch is still the unparalleled launchpad. Why? Because the conference isn’t just about technology; it’s about market validation.

History is our guide. Companies that launched at TechCrunch Disrupt didn’t just have clever tech; they had a credible narrative for how they would fundamentally change human behaviour, capture mindshare, and dominate a market. The intensity of the Startup Battlefield 200, where over 200 hand-selected, early-stage entrepreneurs compete, forces founders to distil their vision into a five-minute pitch that is laser-focused on value.

This focus is the very thing that the venture capital community is desperate for right now. Investors are no longer underwriting the risk of building a foundational LLM—that race is lost to a handful of giants. They are now hunting for the applications that will generate massive ROI on top of that infrastructure. When a respected publication like techcrunch.com reports on a debut, it signals to the world’s most influential VCs—who are all in attendance—that this isn’t science fiction; it’s a Series A waiting to happen.

The successful TechCrunch Disrupt 2025 startup will not have a “better model.” It will have a better system—a goal-driven Agent that can execute, self-correct, and deliver measurable business outcomes without constant human hand-holding. This is the transition from AI as a fancy word processor to AI as a hyper-competent, autonomous employee.

Conclusion: The Era of Doing

For years, the LLM kings have commanded us with the promise of intelligence. We’ve been wowed by their ability to write sonnets, simulate conversations, and generate images. But a truly disruptive technology doesn’t just talk about solving a problem; it solves it.

The Agentic AI revolution marks the transition from the Era of Talking to the Era of Doing.

The biggest LLM is now just a powerful but inert, brain—a resource to be leveraged. The true innovation is in the nervous system, the memory, and the self-correction loop that transforms that raw intelligence into measurable, scalable, and autonomous value.

Will this new era, defined by goal-driven, Agentic AI, be the one that finally breaks the LLM monopoly and truly disrupts Silicon Valley? Let us know your thoughts below.